UK manufacturers see AI as key to future industrial inspection

Posted on 25 January, 2019 by Member News

A group of high-profile representatives from across the aerospace, automotive and engineering industries are calling for the widespread use of artificial intelligence (AI) technology during the industrial inspection process to help overcome the challenges associated with manual inspection.

Above:

Digital reconstructions of CFMS demonstrators using AI for Industrial Inspection.

Speakers from Airbus, Jaguar Land Rover and Rolls Royce, amongst others, used the Artificial Intelligence for Industrial Inspection (AI4II) event – hosted by the Centre for Modelling & Simulation (CFMS) in collaboration with the Bristol Robotics Lab – to highlight the need to replace manual inspection of high value components, such as aircraft wings or engines, with automated technology. The panel discussed AI-based technology as a means of overcoming high labour costs, human error and health and safety concerns – all largely associated with the need to speed up the inspection process in line with growing demand for high quality machinery that lasts.

When sufficiently trained, AI4II can identify defects in high value components using a combination of computer vision and AI technologies, saving manufacturers both time and money and freeing up their engineers to repair faults rather than identify them.

Oliver Grellou, Non-Destructive Testing and Mechanical Testing Engineer at Airbus, said: “Full- scale, manual testing of real aircraft is a massive drain on our resources and needs to be eliminated in favour of more cost-effective, virtual inspections. Manual inspections of a wing, for example, can often be dangerous for our engineers who climb on or in confined spaces. Introducing smarter evaluation of these components with automated technology will be revolutionary for us and we hope to roll out this technology across our production processes in 2019.”

Dr. Iris Fermin, Innovation Lead Engineer at JLR, said: “Given the multiple specifications needed to make just one vehicle, our production lines are incredibly demanding, complex and hugely susceptible to human error. Using artificial intelligence to identify faults with our vehicles on the production line before they get to the dealers would be hugely beneficial in terms of both time and revenue saved. Our aim for the future would be that quality of cars can be checked solely by autonomous technology.”

Dr. Bilal Nassar, Computer Vision Specialist at Rolls Royce, said: “The Innovation Hub at Rolls Royce explores radical new technologies and business concepts across AI and computer vision, which reflect the need for automated inspection across UK manufacturing. By training AI technology to quickly investigate our components, we are transforming the way we work, freeing up resources which we can put into delivering new and exciting projects.”

CFMS, which is an independent, not-for-profit specialist in digital engineering capability, has been developing three demonstrators, one of which it presented to the panel and audience at the AI4II event held at Bristol and Bath Science Park. The demonstrators combine computer vision and AI technologies to automate the manual inspection process and counteract some of the challenges associated with manual inspection.

Kiran Krishnamurthy, AI domain specialist at CFMS, said: “Manufacturers are acutely aware of the problems faced when carrying out a manual industrial inspection, as demonstrated through a number of case studies at our event. AI is an opportunity to introduce innovation and new technology to the visual industrial inspection process, offering an automated, highly reliable, digital solution to these sector specific challenge. This has led for calls to rapidly replace the archaic manual inspection process with new automated technology.”

Speakers at the event also included Bristol Robotics Laboratory’s Mark Hansen and Scorpion Vision’s Julian Parfitt, who discussed data collection, deep learning technology and the cross-sector application of autonomous inspection.